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  • Featured in Physics
  • Open Access

Improving Quantum Machine Learning via Heat-Bath Algorithmic Cooling

Nayeli A. Rodríguez-Briones1,2,* and Daniel K. Park3,4,5,†

  • *Contact author: nayeli.briones@tuwien.ac.at
  • †Contact author: dkd.park@yonsei.ac.kr
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PRX Quantum 7, 010350 – Published 12 March, 2026

DOI: https://doi.org/10.1103/24n3-tskj

Abstract

This work introduces an approach rooted in quantum thermodynamics to enhance sampling efficiency in quantum machine learning (QML). We propose conceptualizing quantum supervised learning as a thermodynamic cooling process. Building on this concept, we develop a quantum refrigerator protocol that enhances sample efficiency during training and prediction without the need for Grover iterations or quantum phase estimation. Inspired by heat-bath algorithmic cooling protocols, our method alternates entropy compression and thermalization steps to decrease the entropy of qubits, increasing polarization toward the dominant bias. This technique minimizes the computational overhead associated with estimating classification scores and gradients, presenting a practical and efficient solution for QML algorithms compatible with noisy intermediate-scale quantum devices.

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synopsis

Cool Qubits Make Faster Decisions

Published 12 March, 2026

Applying thermodynamic principles makes quantum machine-learning protocols more efficient.

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Popular Summary

Quantum machine learning (QML) algorithms rely on repeated measurements, which can make them slow and resource-intensive on quantum hardware. This sampling overhead is a major bottleneck for using quantum classifiers in practice. To address this, we draw inspiration from quantum thermodynamics, treating learning as a cooling process. Based on this idea, we develop bidirectional quantum refrigerators that amplify the signal used by a quantum classifier to distinguish different classes, without knowing the correct answer in advance. This reduces the number of measurements needed for both training and prediction, while remaining compatible with near-term quantum devices. Our method works directly on quantum states and applies broadly to variational quantum classifiers and related models. More broadly, this work shows how thermodynamic ideas can offer tools for data processing in QML, opening fresh directions for making quantum algorithms more practical and scalable.

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PRX Quantum 7, 010350– Published 12 March, 2026
  • Received 10 January 2025
  • Revised 3 December 2025
  • Accepted 4 February 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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